AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing empirical relationships with field measurements across a diversity of sites.

精准预测群落与生态系统对全球变化的响应，有赖于明晰植物功能性状的时空变异规律。美国国家生态观测站网络（National Observatory Ecological Network, NEON）的机载观测平台（Airborne Observation Platform, AOP）可在众多野外站点获取空间分辨率为1米的高光谱影像及配套数据产品，支持高精度的性状制图。然而，此类数据的可靠性依赖于与原位野外实测数据建立严谨的关联关系。
本研究通过与中生高草草原的大范围野外实测数据对比，验证了NEON公开可得的AOP衍生数据产品的精度，包括叶面积指数、总生物量、生态系统结构（冠层高度模型，Canopy Height Model, CHM）以及冠层氮含量。结果显示，AOP衍生数据产品与对应野外实测值的相关性普遍较弱，或无显著关联；其中冠层氮含量与地面氮素实测值的关联最弱，冠层高度模型与地面冠层高度实测值的相关性同样较差。
本研究还采用偏最小二乘回归模型，对比了直接使用完整反射光谱（380~2500 nm）与使用衍生数据产品预测植被性状的效果。在本次考察的8个性状中，仅氮含量的决定系数（R²）超过0.25。所有植被性状的预测决定系数范围为0.08~0.29，预测均方根误差范围为14%~64%。
研究结果表明，当前可用的AOP衍生数据产品可靠性不足，至少在该草原站点如此，若不开展广泛的地面验证则不应直接使用。基于完整反射光谱的建模方法或许更具应用前景，但仍需进一步评估野外与AOP数据的空间尺度匹配性，同时建议开展数据校正与预处理以提升数据质量。此外，草原生境可能对机载光谱遥感尤为具有挑战性，原因在于其小尺度范围内的高物种多样性、植物群落混合功能型，以及干扰与资源可获得性的异质镶嵌分布。
遥感观测是解析时空尺度生态格局的最具潜力的手段之一，但吸引多元化的NEON数据用户群体参与相关研究的契机，有赖于在多样站点中建立遥感数据与野外实测值的实证关联。